Will AI Completely Eliminate Human Involvement In Testing?

The rise of the machines in the testing arena

Artificial Intelligence started in the 1950s, picked up pace steadily, braved the AI winters, and now, it is omnipresent in different fields like defense, medicine, engineering, software development, data analytics, etc. 

A survey was conducted for the World quality report, about how the organizations plan to utilize AI in their QA activities. The results were recorded in the World quality report 2020-2021 and are represented in the following chart.

Approximately 84% of respondents had AI as a part of their growth plan. The rest of the survey portrayed a positive picture for AI usage in software testing. It is quite evident from the above survey that artificial intelligence holds the key to an industrial revolution, with more and more organizations leaning towards using AI in various operations. This has opened new avenues for software testing to ride the AI wave and accelerate the CI/CD/CT pipeline with guaranteed high-quality results.

The following figure gives a quick overview of how AI is used to improvise software testing

Learning: It involves understanding the testing process, codebase, underlying algorithms, data bank, etc. to fully equip the AI tool with the knowledge to apply in the testing.

Application: Once the AI tool is equipped with the knowledge, it can then apply its learnings for test generation, execution, maintenance, and test result analysis.

Continuous improvement: It is the key to AI enhancement. As the AI tool usage grows, so does the data and scenarios at its disposal from which AI can learn and evolve, and consequently apply its knowledge to further improve the testing process.

In nutshell, AI application in its current state equips itself for predictions and decision making based on the learnings from a set of predefined algorithms and available data. Thereby, it aids in improvising automated testing tools by speeding up the entire testing process with precision. 

But can AI completely take over software testing, thus eliminating any kind of human involvement?

The following figure gives a quick overview of where the balance is tipped in AI’s favor and where the humans have an upper hand.

Where AI wins

• Test case generation: Test case generation with AI saves a significant amount of time and effort. It also renders scalability to software testing. 
• Test data generation: AI can generate a large volume of test data based on the past trends within a matter of seconds, which otherwise can take more time if left for manual work.
• Test case maintenance:  AI can dynamically understand the changes made to the application and modify the testing scope accordingly.
• Predictive analysis: AI certainly has an advantage when it comes to analyzing a huge amount of test results in a short time. It can scan, analyze and share the results along with the recommended course of action with precision.

We have a detailed blog that covers the benefits of AI testing and intelligent automation. Click here to read more.

Where humans are still needed

• Edge test cases: There might be certain test scenarios where a judgment call needs to be taken. If AI does not have enough data and learnings from the past, it may falter. That is when human intervention is critical.
• Complex unit test cases: Unit testing for complex business logic can be tricky. AI can simply generate a unit test case based on the code it has been fed. It cannot understand the intended functionality of the module. So if there is a flaw in the programming logic then the unit test may produce an undesired result. This is when the developers have to step in. 
• Usability testing: AI can test any system “mechanically”, but the end-user takes the final call when it comes to addressing the usability of the software.

AI, in general, faces certain roadblocks in its software testing journey. We have elaborated on those challenges in another blog: “Challenges in AI testing”. Read it to have a deeper insight on the subject.

Best of both worlds – AI and Human brilliance with Webomates

Let us go back and refer to the survey mentioned earlier in this blog. While a large % of respondents are still contemplating the usage of AI in various parts of the testing process, we have already made several breakthroughs with 14 AI engines incorporated in our platform Webomates CQ.

Webomates CQ can make life easier for organizations looking for comprehensive TaaS tools powered by Intelligent Automation. 

We provide services that can help organizations in generating and automating the right test cases using the AI Modeler engine. Our patented AI Test Strategy and creator helps in devising a well-rounded test strategy for the software application. At the same time leveraging the competency of our experienced and efficient QA team to work on the tests that may need manual intervention, like edge test cases or usability testing.

Webomates CQ uses a normalized test case modal approach and guarantees that the test cases are self-healed and retested to reflect any changes within the same regression, typically within 24 hours.

Webomates’ Intelligent Analytics improvises the testing process by providing a continuous feedback loop of defects to requirements.

AI fills the gap where manual/automated testing lacks, but it certainly cannot replace humans completely. The role and qualifications of test engineers may evolve over some time to work in tandem with AI. For example, AI test engineers and data scientists will become an integral part of the software testing ecosystem.

Our team is highly qualified and suitably certified to aid and address the predicaments that organizations may face in AI test automation. If you are interested in knowing how we manage our team and work, please click here to have a peek at our agile process.

If this has piqued your interest and you want to know more, then please click here and schedule a demo, or reach out to us at info@webomates.com.

If you like this blog series please like/follow us Webomates or Aseem.

Tags: AI Testing, Artificial Intelligence, Intelligent automation, Test Automation